Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2020, Cilt: 3 Sayı: 2, 99 - 111, 28.08.2020

Fatma Betül Akyol Oğuz Altun

https://doi.org/10.35377/saucis.03.02.722670
Cited By: 4

Öz

Teşekkür

Bu çalışmada rahim ağzı kanseri ve hastalığın teşhisi hakkında bilgi veren İstanbul Zeynep Kâmil Kadın ve Çocuk Hastalıkları Eğitim ve Araştırma Hastanesi Patoloji Bölümü’ne teşekkür ederim.

Kaynakça

  • "National Institutes of Health homepage", 2020. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer). [Accessed: 01-Feb-2020].
  • Cancer Today, "Global Cancer Observatory homepage", 2018. [Online]. Available: http://gco.iarc.fr/today/online-analysis-table. [Accessed: 02-Feb-2020].
  • DB. Cooper, C.E. McCathran, "Cervical Dysplasia", StatPearls [Internet]. StatPearls Publishing, 2019.
  • RJ. Kurman, D. Solomon, The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses. New York: Springer-Verlag, 1994.
  • S.E. Waggoner, "Cervical cancer", The Lancet, vol. 361, no. 9376, pp. 2217-2225, 2003.
  • T. Bilal, J. Dias, and N. Werghi, "Classification of cervical-cancer using pap-smear images: a convolutional neural network approach", Annual Conference on Medical Image Understanding and Analysis, Springer, Cham, 2017.
  • M.E. Plissiti, N. Christophoros, and A. Charchanti, "Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering", IEEE Transactions on information technology in biomedicine, vol. 15, no.2, pp. 233-241, 2010.
  • P. Wang, L. Wang, Y. Li, Q. Song, S. Lv, X. Hu, “Automatic cell nuclei segmentation and classification of cervical Pap smear images”, Biomedical Signal Processing and Control, vol. 48, pp. 93-103, 2019.
  • Y. Marinakis, G. Dounias, and J. Jantzen, "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification", Computers in Biology and Medicine, vol. 39, no.1, pp. 69-78, 2009.
  • A. GençTav, S. Aksoy, and S. Önder, "Unsupervised segmentation and classification of cervical cell images", Pattern recognition, vol. 45, no.12, pp. 4151-4168, 2012.
  • H.A. Phoulady, "A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images", Computerized Medical Imaging and Graphics, vol. 59, pp. 38-49, 2017.
  • K.P. Win, Y. Kitjaidure, K. Hamamoto, T. Myo Aung,"Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images", Appl. Sci., vol. 10, no.5, pp.1800, 2020.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images", Biomedical engineering online, vol.18, no.1, pp.16, 2019.
  • V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
  • M. Pal, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, vol.26, no.1, pp. 217-222, 2005.
  • M.W. Gardner, S.R. Dorling, "Artificial neural networks (the multilayer perceptron) —a review of applications in the atmospheric sciences", Atmospheric Environment, vol. 32, no. 14–15, pp. 2627-2636,1998.
  • Y. Liao, V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection", Computers & security, vol. 21, no.5, pp. 439-448, 2002.
  • M. M. Saritas, A. Yasar, "Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification", International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 2, pp. 88-91, 2019.
  • S.K. Shevade, S. S. Keerthi, "A simple and efficient algorithm for gene selection using sparse logistic regression", Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003.
  • MDE-lab, "MDE-lab downloadspage,"2011.[Online]. Available: http://mde-lab.aegean.gr/downloads. [Accessed: 30-Sep-2019].
  • J. Jantzen, J. Norup, Jonas, G. Dounias, B. Bjerregaard, "Pap-smear Benchmark Data For Pattern Classification", Nature Inspired Smart Information Systems (NiSIS), pp. 1-9, 2005.
  • N. Nill, “A visual model weighted cosine transform for image compression and quality assessment”, IEEE Transactions on communications, vol.33, no. 6, pp. 551-557, 1985.
  • M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, A. Charchanti, SIPAKMED: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images, IEEE International Conference on Image Processing (ICIP) 2018, Athens, Greece, 7-10 October 2018.
  • H. Demirel, G. Anbarjafari, "Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement", IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, 2011.
  • H. Demirel, G. Anbarjafari, “Image resolution enhancement by using discrete and stationary wavelet decomposition”, IEEE transactions on image processing, vol. 20, no. 5, pp. 1458-1460, 2010.
  • E.S. Cibas, B.S. Ducatman, Cytology E-Book: Diagnostic principles and clinical correlates, Elsevier Health Sciences, 2013.
  • "Eurocytology Cervical Cytology homepage", 2020. [Online]. Available: https://www.eurocytology.eu/en/course/1292. [Accessed: 15-Mar-2020].
  • T. Chankong, N. Theera-Umpon, & S. Auephanwiriyakul, "Automatic cervical cell segmentation and classification in Pap smears", Computer methods and programs in biomedicine, vol.113, no.2, pp.539-556, 2014.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm", Informatics in Medicine Unlocked, vol.14, pp. 23-33, 2019.
  • L. Zhang, L. Lu, I. Nogues, R.M. Summers, S. Liu, & J. Yao, “DeepPap: deep convolutional networks for cervical cell classification”, IEEE journal of biomedical and health informatics, vol. 21, no.6, pp. 1633-1643, 2017.

Detection of Cervix Cancer from Pap-smear Images

Yıl 2020, Cilt: 3 Sayı: 2, 99 - 111, 28.08.2020

Fatma Betül Akyol Oğuz Altun

https://doi.org/10.35377/saucis.03.02.722670
Cited By: 4

Öz

Pap-smear test is used to detect cervical cancer, which ranks fourth in the ranking of cancer diseases in women worldwide. In this study, it is aimed to design a computer based decision system that can detect cervical cancer at an early stage. Normal and abnormal cells are found in the cervix images obtained as a result of the pap-smear test and the abnormal cells are marked on the image. The features extracted from the images were examined with pathologists and a dataset was created. For each of the 917 images in the Herlev dataset, these features were extracted and stored in a dataset. Support Vector Machines (SVM), Naive Bayes, Random Forest (RF), Multilayer Perceptron (MLP), Logistic Regression (LR), K- Nearest Neighbor (KNN) methods were applied to the created dataset, and accuracy values between 83% and 92% were obtained.

Anahtar Kelimeler

Cervical cancer Pap-smear Segmentation Classification

Kaynakça

  • "National Institutes of Health homepage", 2020. [Online]. Available: https://www.cancer.gov/about-cancer/understanding/what-is-cancer). [Accessed: 01-Feb-2020].
  • Cancer Today, "Global Cancer Observatory homepage", 2018. [Online]. Available: http://gco.iarc.fr/today/online-analysis-table. [Accessed: 02-Feb-2020].
  • DB. Cooper, C.E. McCathran, "Cervical Dysplasia", StatPearls [Internet]. StatPearls Publishing, 2019.
  • RJ. Kurman, D. Solomon, The Bethesda System for Reporting Cervical/Vaginal Cytologic Diagnoses. New York: Springer-Verlag, 1994.
  • S.E. Waggoner, "Cervical cancer", The Lancet, vol. 361, no. 9376, pp. 2217-2225, 2003.
  • T. Bilal, J. Dias, and N. Werghi, "Classification of cervical-cancer using pap-smear images: a convolutional neural network approach", Annual Conference on Medical Image Understanding and Analysis, Springer, Cham, 2017.
  • M.E. Plissiti, N. Christophoros, and A. Charchanti, "Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering", IEEE Transactions on information technology in biomedicine, vol. 15, no.2, pp. 233-241, 2010.
  • P. Wang, L. Wang, Y. Li, Q. Song, S. Lv, X. Hu, “Automatic cell nuclei segmentation and classification of cervical Pap smear images”, Biomedical Signal Processing and Control, vol. 48, pp. 93-103, 2019.
  • Y. Marinakis, G. Dounias, and J. Jantzen, "Pap smear diagnosis using a hybrid intelligent scheme focusing on genetic algorithm based feature selection and nearest neighbor classification", Computers in Biology and Medicine, vol. 39, no.1, pp. 69-78, 2009.
  • A. GençTav, S. Aksoy, and S. Önder, "Unsupervised segmentation and classification of cervical cell images", Pattern recognition, vol. 45, no.12, pp. 4151-4168, 2012.
  • H.A. Phoulady, "A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images", Computerized Medical Imaging and Graphics, vol. 59, pp. 38-49, 2017.
  • K.P. Win, Y. Kitjaidure, K. Hamamoto, T. Myo Aung,"Computer-Assisted Screening for Cervical Cancer Using Digital Image Processing of Pap Smear Images", Appl. Sci., vol. 10, no.5, pp.1800, 2020.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "A pap-smear analysis tool (PAT) for detection of cervical cancer from pap-smear images", Biomedical engineering online, vol.18, no.1, pp.16, 2019.
  • V. Vapnik, The nature of statistical learning theory. New York: Springer-Verlag, 1995.
  • M. Pal, "Random forest classifier for remote sensing classification", International Journal of Remote Sensing, vol.26, no.1, pp. 217-222, 2005.
  • M.W. Gardner, S.R. Dorling, "Artificial neural networks (the multilayer perceptron) —a review of applications in the atmospheric sciences", Atmospheric Environment, vol. 32, no. 14–15, pp. 2627-2636,1998.
  • Y. Liao, V. R. Vemuri, "Use of k-nearest neighbor classifier for intrusion detection", Computers & security, vol. 21, no.5, pp. 439-448, 2002.
  • M. M. Saritas, A. Yasar, "Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification", International Journal of Intelligent Systems and Applications in Engineering, vol. 7, no. 2, pp. 88-91, 2019.
  • S.K. Shevade, S. S. Keerthi, "A simple and efficient algorithm for gene selection using sparse logistic regression", Bioinformatics, vol. 19, no. 17, pp. 2246-2253, 2003.
  • MDE-lab, "MDE-lab downloadspage,"2011.[Online]. Available: http://mde-lab.aegean.gr/downloads. [Accessed: 30-Sep-2019].
  • J. Jantzen, J. Norup, Jonas, G. Dounias, B. Bjerregaard, "Pap-smear Benchmark Data For Pattern Classification", Nature Inspired Smart Information Systems (NiSIS), pp. 1-9, 2005.
  • N. Nill, “A visual model weighted cosine transform for image compression and quality assessment”, IEEE Transactions on communications, vol.33, no. 6, pp. 551-557, 1985.
  • M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, A. Charchanti, SIPAKMED: A new dataset for feature and image based classification of normal and pathological cervical cells in Pap smear images, IEEE International Conference on Image Processing (ICIP) 2018, Athens, Greece, 7-10 October 2018.
  • H. Demirel, G. Anbarjafari, "Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement", IEEE Transactions on Geoscience and Remote Sensing, vol. 49, no. 6, pp. 1997-2004, 2011.
  • H. Demirel, G. Anbarjafari, “Image resolution enhancement by using discrete and stationary wavelet decomposition”, IEEE transactions on image processing, vol. 20, no. 5, pp. 1458-1460, 2010.
  • E.S. Cibas, B.S. Ducatman, Cytology E-Book: Diagnostic principles and clinical correlates, Elsevier Health Sciences, 2013.
  • "Eurocytology Cervical Cytology homepage", 2020. [Online]. Available: https://www.eurocytology.eu/en/course/1292. [Accessed: 15-Mar-2020].
  • T. Chankong, N. Theera-Umpon, & S. Auephanwiriyakul, "Automatic cervical cell segmentation and classification in Pap smears", Computer methods and programs in biomedicine, vol.113, no.2, pp.539-556, 2014.
  • W. William, A. Ware, A.H. Basaza-Ejiri, & J. Obungoloch, "Cervical cancer classification from Pap-smears using an enhanced fuzzy C-means algorithm", Informatics in Medicine Unlocked, vol.14, pp. 23-33, 2019.
  • L. Zhang, L. Lu, I. Nogues, R.M. Summers, S. Liu, & J. Yao, “DeepPap: deep convolutional networks for cervical cell classification”, IEEE journal of biomedical and health informatics, vol. 21, no.6, pp. 1633-1643, 2017.
Toplam 30 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka
Bölüm Makaleler
Yazarlar

Fatma Betül Akyol 0000-0002-3836-1317

Oğuz Altun 0000-0001-8823-3148

Yayımlanma Tarihi 28 Ağustos 2020
Gönderilme Tarihi 18 Nisan 2020
Kabul Tarihi 21 Mayıs 2020
Yayımlandığı Sayı Yıl 2020Cilt: 3 Sayı: 2

Kaynak Göster

IEEE F. B. Akyol ve O. Altun, “Detection of Cervix Cancer from Pap-smear Images”, SAUCIS, c. 3, sy. 2, ss. 99–111, 2020, doi: 10.35377/saucis.03.02.722670.

Cited By

Deep learning-based approaches for robust classification of cervical cancer
Neural Computing and Applications
https://doi.org/10.1007/s00521-023-08757-w
Cervical Cancer Prediction Using SMOTE Algorithm and Machine Learning Approaches
Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.21597/jist.1222764
A Deep Neural Network for Cervical Cell Classification Based on Cytology Images
IEEE Access
https://doi.org/10.1109/ACCESS.2022.3230280
Derin öğrenme temelli nesne tespiti algoritmaları kullanılarak kişiye özgü reklam sunulması
Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi
https://doi.org/10.25092/baunfbed.878224
 Kapak Resmi İndir

    Sakarya University Journal of Computer and Information Sciences in Applied Sciences and Engineering: An interdisciplinary journal of information science